The present invention generally deals with systems and method of managing water.
With states across the country dealing with unprecedented levels of drought, water utilities are scrambling to find effective ways to analyze water usage within their districts and target their conservation efforts. In order to do this, they need to create an accurate water budget that shows them how much water each land parcel in their water district needs given the evapotranspiration (ET) rates of its land cover composition. An evapotranspiration rate is the sum of evaporation and plant transpiration from the Earth's land and ocean surface to the atmosphere. Evaporation accounts for the movement of water to the air from sources such as the soil, canopy interception, and waterbodies.
This information, combined with actual customer water use data, provides the water district with information on where to target water conservation marketing efforts. Without technology, this process must be done by manually surveying each parcel, which is a costly, time consuming, and error-prone process.
A conventional solution has three main stages: water budget calculation, result display, and comparison between the water budget and the customer water use data.
Satellite imagery is conventionally used to determine the square footage of each parcel by land cover type, e.g., trees, grass, natural water body, man-made surface, man-made water body, etc. The final water budget will be calculated by multiplying the area of each parcel's land cover type by the associated ET rate and combining these products. These calculations essentially show how much of each land cover type is present on each parcel (i.e., blacktop, grass, swimming pool, etc.) and therefore how much water each parcel should need. For example, trees may have a higher ET rate than a blacktop, so a parcel of land of trees will need more water than a parcel of land of blacktop.
A parcel's water budget is then compared with the actual water use taken from customer water meter data. Parcels with large discrepancies indicate abnormal water use that can be targeted for further outreach and investigation. This solution uses satellite imagery, geo-located parcel data, customer water use data, and an external source for ET rates.
A conventional system and method for managing water will now be described with reference to FIGS. 1-6.
FIG. 1 illustrates a conventional system 100 for managing water.
As shown in the figure, system 100 includes resource managing component 102 and a network 104. Resource managing component 102 includes a database 106, a controlling component 108, an accessing component 110, a communication component 112, a vegetation index component 114, a classification component 116, a zonal statistics component 118, a water budget component 120 and a delta component 122.
In this example, database 106, controlling component 108, accessing component 110, communication component 112, vegetation index component 114, classification component 116, zonal statistics component 118, water budget component 120 and delta component 122 are illustrated as individual devices. However, in some embodiments, at least two of database 106, controlling component 108, accessing component 110, communication component 112, vegetation index component 114, classification component 116, zonal statistics component 118, water budget component 120 and delta component 122 may be combined as a unitary device. Further, in some embodiments, at least one of database 106, controlling component 108, accessing component 110, communication component 112, vegetation index component 114, classification component 116, zonal statistics component 118, water budget component 120 and delta component 122 may be implemented as a computer having tangible computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such tangible computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer. Non-limiting examples of tangible computer-readable media include physical storage and/or memory media such as RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. For information transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer may properly view the connection as a computer-readable medium. Thus, any such connection may be properly termed a computer-readable medium. Combinations of the above should also be included within the scope of computer-readable media.
Controlling component 108 is in communication with each of accessing component 110, communication component 112, vegetation index component 114, classification component 116, zonal statistics component 118, water budget component 120 and delta component 122 by communication channels (not shown). Controlling component 108 may be any device or system that is able to control operation of each of accessing component 110, communication component 112, vegetation index component 114, classification component 116, zonal statistics component 118, water budget component 120 and delta component 122.
Accessing component 110 is arranged to bi-directionally communicate with database 106 via a communication channel 124 and is arranged to bi-directionally communicate with communication component 112 via a communication channel 126. Accessing component 110 is additionally arranged to communicate with vegetation index component 114 via a communication channel 128, to communicate with classification component 116 via a communication channel 130, to communicate with zonal statistics component 118 via a communication channel 132, to communicate with water budget component 120 via a communication channel 134 and to communicate with delta component 122 via a communication channel 136. Accessing component 110 may be any device or system that is able to access data within database 106 directly via communication channel 124 or indirectly, via communication channel 126, communication component 112, communication channel 138, network 104 and communication channel 140.
Communication component 112 is additionally arranged to bi-directionally communicate with network 104 via a communication channel 138. Communication component 112 may be any device or system that is able to bi-directionally communicate with network 104 via communication channel 138.
Network 104 is additionally arranged to bi-directionally communicate with database 106 via a communication channel 140. Network 104 may be any of known various communication networks, non-limiting examples of which include a Local Area Network (LAN), a Wide Area Network (WAN), a wireless network and combinations thereof. Such networks may support telephony services for a mobile terminal to communicate over a telephony network (e.g., Public Switched Telephone Network (PSTN). Non-limiting example wireless networks include a radio network that supports a number of wireless terminals, which may be fixed or mobile, using various radio access technologies. According to some example embodiments, radio technologies that can be contemplated include: first generation (1G) technologies (e.g., advanced mobile phone system (AMPS), cellular digital packet data (CDPD), etc.), second generation (2G) technologies (e.g., global system for mobile communications (GSM), interim standard 95 (IS-95), etc.), third generation (3G) technologies (e.g., code division multiple access 2000 (CDMA2000), general packet radio service (GPRS), universal mobile telecommunications system (UMTS), etc.), 4G, etc. For instance, various mobile communication standards have been introduced, such as first generation (1G) technologies (e.g., advanced mobile phone system (AMPS), cellular digital packet data (CDPD), etc.), second generation (2G) technologies (e.g., global system for mobile communications (GSM), interim standard 95 (IS-95), etc.), third generation (3G) technologies (e.g., code division multiple access 2000 (CDMA2000), general packet radio service (GPRS), universal mobile telecommunications system (UMTS), etc.), and beyond 30 technologies (e.g., third generation partnership project (3GPP) long term evolution (3GPP LTE), 3GPP2 universal mobile broadband (3GPP2 UMB), etc.).
Complementing the evolution in mobile communication standards adoption, other radio access technologies have also been developed by various professional bodies, such as the Institute of Electrical and Electronic Engineers (IEEE), for the support of various applications, services, and deployment scenarios. For example, the IEEE 802.11 standard, also known as wireless fidelity (WiFi), has been introduced for wireless local area networking, while the IEEE 802.16 standard, also known as worldwide interoperability for microwave access (WiMAX) has been introduced for the provision of wireless communications on point-to-point links, as well as for full mobile access over longer distances. Other examples include Bluetooth™, ultra-wideband (UWB), the IEEE 802.22 standard, etc.
Vegetation index component 114 is additionally arranged to communicate with classification component 116 via a communication channel 142. Vegetation index component 114 may be any device or system that is able to generate a vegetation index, or a normalized difference vegetation index (NDVI). An NDVI is a simple graphical indicator that can be used to analyze remote sensing measurements, typically not necessarily form a space platform, and assess whether the target being observed contains live green vegetation or not. In an example embodiment, a normalized difference vegetation index is generated using the following equation:(νNIR−νR)/(νNIR+νR),  (1)where νNIR is the near infrared band and where νR is the red band.
Classification component 116 is additionally arranged to communicate with zonal statistics component 118 via a communication channel 144. Classification component 116 may be any device or system that is able to classify each pixel, or group of pixels, of an image as one of the group of predefined land cover classes. In some non-limiting examples, classification component 116 is able to classify each pixel as one of the group consisting of grass, a tree, a shrub, a man-made surface, a man-made pool, a natural water body and artificial turf.
Zonal statistics component 118 is additionally arranged to communicate with water budget component 120 via a communication channel 146. Zonal statistics component 118 may be any device or system that is able to generate a land cover classification per parcel of land. For example, zonal statistics component 118 may determine that a specific county, as the parcel of land, has 38% tree cover, 18% shrub cover, 16% blacktop cover, 12% grass cover, 8% natural water cover and 8% man-made structure cover based on the classification of the pixels of the image within the county as defined by the parcel data. In some embodiments, zonal statistics component 11 may determine the percentages of cover by dividing the number of pixels of the image within the parcel by the number of pixels of a particular type of classification (cover).
Water budget component 120 is additionally arranged to communicate with delta component 122 via a communication channel 148. Water budget component 120 may be any device or system that is able to calculate a water budget per parcel of land in view of the evapotranspiration rates for the parcel of land. For example, water budget component 118 may determine the water budget of the county discussed above (having 38% tree cover, 18% shrub cover, 16% blacktop cover, 12% grass cover, 8% natural water cover and 8% man-made structure cover) based on the ET rates of trees, shrubs, blacktop, grass, natural water and man-made structures.
Delta component 122 is additionally arranged to communicate with communication component 112 via a communication channel 150. Delta component 122 may be any device or system that is able to generate a difference of an amount of water, Δ, by comparing the water budget with the water meter readings within the parcel of land. For example, delta component 122 may determine Δ of a parcel of land based on the following:Δ=wm−ΣET,  (2)where wm is the total amount of metered water in the parcel of land, ET is the ET rate of a pixel within the parcel of land.
Communication channels 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148 and 150 may be any known wired or wireless communication channel.
Operation of system 100 will now be described with reference to FIGS. 2-6.
FIG. 2 illustrates a conventional method 200 of managing water.
As shown in the figure, method 200 starts (S202) and image data is received (S204). For example, as shown in FIG. 1, accessing component 110 retrieves image data from database 106. In some embodiments, accessing component 110 may retrieve the image data directly from database 106 via communication channel 124. In other embodiments, accessing component 110 may retrieve the image data from database 106 via a path of communication channel 124, communication component 112, communication channel 138, network 104 and communication channel 140.
Database 106 may have various types of data stored therein. This will be further described with reference to FIG. 3.
FIG. 3 illustrates an example of database 106 of FIG. 1.
As shown in FIG. 3, database 106 includes an image data database 302, a training data database 304, a parcel data database 306, an evapotranspiration (“ET”) rates database 308 and a water meter database 310.
In this example, image data database 302, training data database 304, parcel data database 306, ET rates database 308 and water meter database 310 are illustrated as individual devices. However, in some embodiments, at least two of image data database 302, training data database 304, parcel data database 306, ET rates database 308 and water meter database 310 may be combined as a unitary device. Further, in some embodiments, at least one of image data database 302, training data database 304, parcel data database 306, ET rates database 308 and water meter database 310 may be implemented as a computer having tangible computer-readable media for carrying or having computer-executable instructions or data structures stored thereon.
Image data database 302 includes image data corresponding to an area of land for which water is to be managed. The image data may be provided via a satellite imaging platform. The image data may include a single band or multi-band image data, wherein the image (of the same area of land for which water is to be managed) is imaged in a more than one frequency. In some embodiments, image data may include 4-band image data, which include red, green, blue and near infrared bands (RGB-NIR) of the same area of land for which water is to be managed. In other embodiments, the image data may include more than 4 bands, e.g., hyperspectral image data. The image data comprises pixels, each of which includes respective data values for frequency (color) and intensity (brightness). The frequency may include a plurality of frequencies, based on the number of bands used in the image data. Further, there may be a respective intensity value for each frequency value.
Training data database 304 includes training data to train a classification component to distinctly classify an image pixel. For example, training data for a 4-band image may include specific 4-band pixels data values associated with each land cover classification. In other words, there may be training data for a pixel associated with an image of a tree and different training data for a pixel associated with a man-made surface such as blacktop.
Parcel data database 306 includes geographically divided portions of the land. This may be provided by government agencies or public utilities. Non-limiting examples of geographically divided portions include country, state, county, township, city or individual land owner borders.
ET rates database 308 includes ET rates for regions. These ET rates may be provided by government agencies or public utilities.
Water meter data database 310 includes water meter readings as provided by government agencies or public utilities.
Returning to FIG. 1, in some cases, database 106 is included in resource managing component 102. However, in other cases, database 106 is separated from resource managing component 102, as indicated by dotted rectangle 110.
As accessing component 110 will be accessing many types of data from database 106, accessing component 110 includes many data managing components. This will be described with greater detail with reference to FIG. 4.
FIG. 4 illustrates an example of accessing component 110 of FIG. 1.
As shown in FIG. 4, accessing component 110 includes a communication component 402, an image data receiving component 404, a training data receiving component 406, a parcel data receiving component 408, an ET rates data receiving component 410 and a water meter data receiving component 412.
In this example, communication component 402, image data receiving component 404, training data receiving component 406, parcel data receiving component 408, ET rates data receiving component 410 and water meter data receiving component 412 are illustrated as individual devices. However, in some embodiments, at least two of communication component 402, image data receiving component 404, training data receiving component 406, parcel data receiving component 408, ET rates data receiving component 410 and water meter data receiving component 412 may be combined as a unitary device. Further, in some embodiments, at least one of communication component 402, image data receiving component 404, training data receiving component 406, parcel data receiving component 408, ET rates data receiving component 410 and water meter data receiving component 412 may be implemented as a computer having tangible computer-readable media for carrying or having computer-executable instructions or data structures stored thereon.
Communication component 402 is arranged to bi-directionally communicate with database 106 via a communication channel 124 and is arranged to bi-directionally communicate with communication component 112 via a communication channel 126.
Communication component 402 is additionally arranged to directionally communicate with image data component 404 via a communication channel 414, to communicate with training data component 406 via a communication channel 416, to communicate with parcel data component 408 via a communication channel 418, to communicate with ET rates data component 410 via a communication channel 420 and to communicate with water meter data component 412 via a communication channel 422. Communication component 402 may be any device or system that is able to access data within database 106 directly via communication channel 124 or indirectly, via communication channel 126, communication component 112, communication channel 138, network 104 and communication channel 140. Image data component 404, training data component 406, parcel data component 408, ET rates data component 410 and water meter data component 412 may each be any device or system that is able to receive data from communication component 402 and to output the received data.
Image data component 402 is additionally arranged to communicate with vegetation index component 114 via communication channel 128. Training data component 406 is additionally arranged to communicate with classification component 116 via communication channel 130. Parcel data component 408 is additionally arranged to communicate with zonal statistics component 118 via communication channel 132. ET rates data component 410 is additionally arranged to communicate with water budget component 120 via communication channel 134. Water meter data component 412 is additionally arranged to communicate with delta component 122 via communication channel 136. Communication channels 414, 416, 418, 420 and 422 may be any known wired or wireless communication channel.
Returning to FIG. 1, at this point accessing component 110 has received the image data. An example of such image data will now be described with reference to FIG. 5.
FIG. 5 illustrates a satellite image 500 of a plot of land.
As shown in the figure, satellite image 500 includes a grass 502, trees 504, man-made surfaces—including building 506 and road 508, and a man-made pool 510.
As for a broad view of method 200, system 100 will be able to determine the amount of water that is received within the area of land within satellite image 500, to determine, with the ET rate of water within the area of land within satellite image 500, the amount of water used (by residents for example) within the area of land within satellite image 500 and to determine a surplus or deficit (Δ) of water within the area of land within satellite image 500.
This will now be continued by returning to FIG. 2.
After the image data is received (S204), a vegetation index is generated (S206). For example, as shown in FIG. 1, accessing component 110 provides the received image data to vegetation index component 114 via communication channel 128. For example, as shown in FIG. 1 accessing component 110 retrieves image data from database 106. As shown in FIG. 3, database 106 provides the image data from image data database 302. As shown in FIG. 4, communication component 402 receives the image data from image data database 302 and provides the image data to image data receiving component 404 via communication channel 414. Returning to FIG. 1, image data receiving component 404 (of accessing component 110) then provides the image data to vegetation index component 114 via communication channel 128.
Vegetation index component 114 generates a NDVI vegetation index for the image data and provides the vegetation index to classification component 116 via communication channel 142.
Returning to FIG. 2, after the vegetation index is generated (S206), classification results are generated (S208). For example, as shown in FIG. 1, accessing component 110 provides the received image data additionally to classification component 116 via communication channel 130. Further, vegetation index component 114 provides the vegetation index to classification component 116 via communication line 142. With the image data from accessing component 110 and with the vegetation index from vegetation index component, classification component 116 classifies each pixel of data as one of many predetermined classes.
For example, returning to FIG. 5, a pixel within image 500 at the location of trees 504 will have colors (frequencies) and intensities indicative of trees. As such, classification component will use information from the vegetation index in addition to the image data for that pixel to classify the pixel as a tree. Similarly, a pixel within image 500 at the location of road 508 will have colors (frequencies) and intensities indicative of a road. As such, classification component will use information from the vegetation index in addition to the image data for that pixel to classify the pixel as a road. This classification continues for each pixel within image 500.
Returning to FIG. 2, after the classification results are generated (S208), training data is received (S210). For example, as shown in FIG. 1 accessing component 110 retrieves training data from database 106. As shown in FIG. 3, database 106 provides the training data from training data database 304. As shown in FIG. 4, communication component 402 receives the training data from training data database 304 and provides the training data to training data receiving component 406 via communication channel 416. Returning to FIG. 1, training data receiving component 406 (of accessing component 110) then provides the training data to classification component 116 via communication channel 130.
It should be noted that in the example discussed above, generating the classification results (S208) is prior to receiving training data (S210). However, in some embodiments, generating the classification results (S208) may occur after receiving training data (S210). Further, in some embodiments, generating the classification results (S208) may occur concurrently with receiving training data (S210).
Returning to FIG. 2, after the training data is received (S210), a final classification is generated (S212). For example, every pixel within the entire image 500 of FIG. 5 will have been classified. This will be described with reference to FIG. 6.
FIG. 6 illustrates a classified image 600 of the plot of land within satellite image 500 of FIG. 5.
As shown in FIG. 6, classified image 600 includes an area 602, an area 604, an area 606, an area 608 and an area 610. Area 602 corresponds to grass 502 of satellite image 500 of FIG. 5. Area 604 corresponds to trees 504 of satellite image 500 of FIG. 5. Area 606 corresponds to building 506 of satellite image 500 of FIG. 5. Area 608 corresponds to road 508 of satellite image 500 of FIG. 5. Area 610 corresponds to man-made pool 510 of satellite image 500 of FIG. 5.
Returning to FIG. 2, after the final classification is generated (S212), parcel data is received (S214). For example, as shown in FIG. 1, accessing component 110 provides the parcel data to zonal statistics component 118 via communication channel 132. For example, as shown in FIG. 1 accessing component 110 retrieves parcel data from database 106. As shown in FIG. 3, database 106 provides the parcel data from parcel data database 306. As shown in FIG. 4, communication component 402 receives the parcel data from parcel data database 306 and provides the parcel data to parcel data receiving component 408 via communication channel 418. Returning to FIG. 1, parcel data receiving component 408 (of accessing component 110) then provides the parcel data to zonal statistics component 118 via communication channel 132.
At this point, the boundaries of land are known by way of the parcel data. These boundaries may include country boundaries, state boundaries, county boundaries, city/town boundaries and boundaries of individually owned parcels of land. These boundaries may be provided by government entities and/or private entities. Zonal statistics component 118 may use the boundaries as identified in the parcel data to establish the land cover per parcel of land.
Returning to FIG. 2, after the parcel data is received (S214) and the land cover has been classified per parcel of land, the land cover by parcel is generated (S216). For example, as shown in FIG. 1
Zonal statistics component 118 then generates the land cover classification per parcel of land. For example, if the image data were to include the image of an entire state, zonal statistics component 118 may be able to generate the land cover classification per county, per town, or even per parcel of land by organizing the land cover classification per county, per town, etc. More particularly, polygons are drawn around each land cover type. The end result is a vector layer of land cover polygons that are then used to calculate area. Zonal statistics is not often used, but is used in more general remote sensing applications. The biggest difference is that zonal statistics are derived directly from the imagery. On the other hand, land cover calculation using vector layers has an intermediary step of transforming the image into a vector layer for each land cover type, and then the area for each vector layer is calculated within the parcel.
Returning to FIG. 2, after the land cover by parcel is generated (S216), the ET rates are received (S218). For example, as shown in FIG. 1, accessing component 110 provides the ET rates data to water budget component 120 via communication channel 134. For example, as shown in FIG. 1 accessing component 110 retrieves ET rates data from database 106. As shown in FIG. 3, database 106 provides the ET rates data from ET rates data database 308. As shown in FIG. 4, communication component 402 receives the ET rates data from ET rates data database 308 and provides the ET rates data to ET rates data receiving component 410 via communication channel 420. Returning to FIG. 1, ET rates data receiving component 410 (of accessing component 110) then provides the ET rates data to water budget component 120 via communication channel 134.
Returning to FIG. 2, after the ET rates are received (S218), the water budget per parcel is generated (S220). For example, as shown in FIG. 1, water budget component 120 determines a water budget per parcel in light of the ET rate of the respective parcel. For example, for purposes of discussion, let the plot of land within image 500 of FIG. 5 be a delineated parcel of land.
At this point of method 200, land cover of the parcel of land within image 500 has been determined. As shown in FIG. 1, zonal statistics component 118 provides the land cover of the parcel of land to water budget component 120 via communication channel 146. Further, the ET rates are known from ET rates database 308. As such, the ET rates of the plot of land within image 500 of FIG. 5 may be determined.
A water budget may be determined with a pre-determined upper threshold of retained water and a predetermined lower threshold of retained water. The retained water is determined by subtracting the amount of evaporated water, as determined from the evapotranspiration rate, from the amount of received water.
Returning to FIG. 2, after water budget per parcel is generated (S220), the water meter readings are received (S222). For example, as shown in FIG. 1, accessing component 110 provides the water meter data to delta component 122 via communication channel 136. For example, as shown in FIG. 1 accessing component 110 retrieves water meter data from database 106. As shown in FIG. 3, database 106 provides the water meter data from water meter data database 310. As shown in FIG. 4, communication component 402 receives the water meter data from water meter data database 310 and provides the water meter data to water meter data receiving component 412 via communication channel 422. Returning to FIG. 1, water meter data receiving component 412 (of accessing component 110) then provides the water meter data to delta component 122 via communication channel 136.
The water meter readings indicate the amount of metered water used in the parcel. For example, in a county, the sum water meter readings of the individual property owners will provide an accurate estimate of the amount of water used and disposed of by the county.
Returning to FIG. 2, after water meter readings are received (S222), the Δ is generated (S224). For example, as shown in FIG. 1, delta component 122 determines a water surplus or water deficit per parcel of land. Water budget component 120 provides the water budget per parcel to delta component 122 via communication line 148. Further, as noted above, accessing component 110 provides the ET rates to delta component via communication channel 136.
The amount of water retained by the land will include the precipitation within the parcel of land minus the metered water, minus the evaporated water, wherein the evaporated water is determined by the ET rate. Typically, it is a goal to maintain a constant amount of retained water, wherein the amount of precipitation is equal to the amount of metered water and evaporated water. In this light, a water budget is based on the amount of precipitation—the amount of water received, and the amount of evaporated water—derived from the ET rates. If the amount of water received is less than the combined amount of metered water and the amount of evaporated water, then the parcel of land will have a water deficit, wherein the Δ for the parcel of land will be negative. If the amount of water received is more than the combined amount of metered water and the amount of evaporated water, then the parcel of land will have a water surplus deficit, wherein the Δ for the parcel of land will be positive.
Returning to FIG. 2, after the Δ is generated (S224), method 200 stops (S226).
A problem with the conventional system discussed above is that classification component 116 may inaccurately classify some pixels because the classification is based solely on the vegetation index. There may be circumstances that non-vegetation has a similar image to vegetation. In such cases, the non-vegetation as imaged by the satellite platform may have a similar vegetation index generated by vegetation index component 114. Therefore, the non-vegetation would incorrectly be classified as vegetation by classification component 116. This would ultimately lead to an incorrect land cover, an incorrect water budget and an incorrect Δ.
Another problem with the conventional system discussed above is there are many available individual classification methods that may be employed classification component 116, wherein each classification method has its strengths and weaknesses. Accordingly, there is no perfect classification method for all images. Therefore, in some cases, many pixels of the image may incorrectly be classified by classification component 116, again which would ultimately lead to an incorrect land cover, an incorrect water budget and an incorrect Δ.
Accordingly, for at least the foregoing reasons there exists a need to provide an improved method and apparatus of managing water.